import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns

Data

We'll build a small program to generate a sample dataset of user that has schema below:

Wallet Address | Transaction Count | Transaction Volume | Avg Transaction Value | Transaction Frequency | Asset Diversity | Liquidity Provision | Staking Activity | Yield Farming Participation | Borrowing Value | Borrowing Frequency | Lending Value | Lending Frequency | Active Periods| Transaction Recency | Historical Activity Trends

import random
import string

def random_wallet_address():
    return "0x" + ''.join(random.choices(string.ascii_letters + string.digits, k=40))

def random_time_frame():
    return random.choice(['Daily', 'Weekly', 'Bi-weekly', 'Monthly', 'Never'])

def random_activity_trend():
    return random.choice(['Increasing', 'Stable', 'Decreasing'])

def random_active_periods():
    periods = [
        'Weekdays 10am-4pm', 'Weekdays 6pm-10pm', 'Weekdays 9am-5pm', 
        'Weekdays 7am-9am', 'Weekends 1pm-5pm', 'Weekends 8pm-12am',
        'Weekends 6pm-10pm', 'Weekdays 11am-3pm', 'Weekends 10am-2pm'
    ]
    return random.choice(periods)

# Generate 100 records
data = []
for _ in range(100):
    record = {
        "Wallet Address": random_wallet_address(),
        "Transaction Count": random.randint(10, 200),
        "Transaction Volume": random.randint(1000, 50000),
        "Avg Transaction Value": random.randint(200, 300),
        "Transaction Frequency": random_time_frame(),
        "Asset Diversity": random.randint(1, 10),
        "Liquidity Provision": random.randint(1000, 20000),
        "Staking Activity": random.randint(500, 10000),
        "Yield Farming Participation": random.randint(0, 15000),
        "Borrowing Value": random.randint(0, 5000),
        "Borrowing Frequency": random_time_frame(),
        "Lending Value": random.randint(0, 7000),
        "Lending Frequency": random_time_frame(),
        "Active Periods": random_active_periods(),
        "Transaction Recency": random.choice(["1 day ago", "2 days ago", "3 days ago", "1 week ago", "2 weeks ago", "1 month ago"]),
        "Historical Activity Trends": random_activity_trend()
    }
    data.append(record)

df = pd.DataFrame(data=data)
df.head(10)

Transform user to vector

$$u_i = f(x_i)$$

$f$:is a transform function.

$x_i$: is a features set of user.

$u_i$: is a vector of specific user.

from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder


def transform_function(df):

    ordinal_features = ["Transaction Frequency", "Historical Activity Trends", "Transaction Recency"]
    ordinal_encoder = OrdinalEncoder(categories=[["Never", "Monthly", "Bi-weekly", "Weekly", "Daily"], ["Decreasing", "Stable", "Increasing"], ["1 day ago", "2 days ago", "3 days ago", "1 week ago", "2 weeks ago", "1 month ago"]])
    df[ordinal_features] = ordinal_encoder.fit_transform(df[ordinal_features])

    onehot_features = ["Active Periods"]
    onehot_encoder = OneHotEncoder()
    onehot_encoded = onehot_encoder.fit_transform(df[onehot_features]).toarray()
    onehot_encoded_df = pd.DataFrame(onehot_encoded, columns=onehot_encoder.get_feature_names_out(onehot_features))

    df = pd.concat([df.drop(columns=["Wallet Address", "Active Periods"]), onehot_encoded_df], axis=1)
    return df 

df = transform_function(df)

pd.options.display.max_colwidth = None
pd.options.display.max_columns = None

df = df.drop(columns=['Lending Frequency', 'Borrowing Frequency'])
df.head()

Similarity

Assume we have a set of users that represented by matrix $P$ where each user is presented by vector $p$. Now, we want to list all of top (eg: 10) user that they are the most similar with user $p_i$.

From matrix $P$, we compute covariance matrix $Q$, that is formed: $$ \begin{aligned} Q = P \cdot P^T \\ \end{aligned} $$

The, the similar score of $p_i$ will be $q_i$, to get the top similarity of $p_i$: $$top_i = argmax(q_i)$$

(we also ignore product $i$ in the top_i as itself similar score is 1)

user_matrix = df.astype(float).to_numpy()

norm_products = user_matrix/np.linalg.norm(user_matrix, axis=1)[:, None]
Q = np.dot(norm_products, norm_products.T)

fig, ax = plt.subplots(figsize = (20, 20))
sns.heatmap(Q, annot=True, fmt='.2f', cmap='RdYlGn')
plt.show()

sns.heatmap(Q[:10, :10], annot=True, fmt='.2f', cmap='RdYlGn')
plt.show()

Classification users

Implement simple classification on this data shows the highest participation in staking...

import pandas as pd
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score
from imblearn.over_sampling import SMOTE

# Assuming the data has already been prepared and encoded as in the previous steps

# Define high participation in staking as above the median
threshold = df['Staking Activity'].quantile(0.8)
df['High Staking Participation'] = (df['Staking Activity'] > threshold).astype(int)

# Features and target
X = df.drop(columns=['Staking Activity', 'High Staking Participation'])
y = df['High Staking Participation']

# Handle imbalanced data
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=0.2, random_state=42)

# Hyperparameter tuning
param_grid = {
    'n_estimators': [100, 200, 300],
    'max_depth': [None, 10, 20, 30],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4]
}

grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42), param_grid=param_grid, cv=3, n_jobs=1, verbose=2)
grid_search.fit(X_train, y_train)

best_clf = grid_search.best_estimator_

# Predictions
y_pred = best_clf.predict(X_test)

# Evaluation
accuracy = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)

print(f'Best Parameters: {grid_search.best_params_}')
print(f'Accuracy: {accuracy}')
print(f'Classification Report:\n{report}')

Fitting 3 folds for each of 108 candidates, totalling 324 fits
[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
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[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.1s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   0.2s
[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   0.2s
Best Parameters: {'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}
Accuracy: 0.90625
Classification Report:
              precision    recall  f1-score   support

           0       0.94      0.88      0.91        17
           1       0.88      0.93      0.90        15

    accuracy                           0.91        32
   macro avg       0.91      0.91      0.91        32
weighted avg       0.91      0.91      0.91        32

	Wallet Address	Transaction Count	Transaction Volume	Avg Transaction Value	Transaction Frequency	Asset Diversity	Liquidity Provision	Staking Activity	Yield Farming Participation	Borrowing Value	Borrowing Frequency	Lending Value	Lending Frequency	Active Periods	Transaction Recency	Historical Activity Trends
0	0x4w5qFhl7wgFLwVE0ZlaHk6sjNWd6xliLmn8vLq97	154	18248	274	Never	5	5926	7232	11620	937	Bi-weekly	1065	Never	Weekdays 11am-3pm	2 weeks ago	Decreasing
1	0x2M6KO5TvGez2LCn4weFdI4RXBRJ3kTVEBWtIK0n0	23	16162	298	Bi-weekly	3	1110	6273	1872	1405	Bi-weekly	312	Bi-weekly	Weekends 8pm-12am	1 week ago	Stable
2	0xbpXxykLr15t73HAexiUlOHzfiwNuxfnCYiiUyFdd	31	42378	252	Bi-weekly	5	12326	9085	3379	1913	Monthly	4942	Never	Weekdays 10am-4pm	1 day ago	Decreasing
3	0x2XGUehw6PlEYe69NZk1Qtf8yMjoylhz1heo1oDJi	141	13233	300	Never	1	19476	8981	2560	2388	Monthly	200	Weekly	Weekends 6pm-10pm	1 month ago	Decreasing
4	0xs0QX4UfexwYd0quzW7P9Hw3uHTTTwG2jx9lkdoRg	112	2435	296	Monthly	7	12749	5968	2967	4038	Weekly	6802	Never	Weekends 10am-2pm	1 day ago	Increasing
5	0xwtXhxzRgb8G1tD065JQkTgqi1HmwYmEnaZYVdzyq	138	42199	218	Bi-weekly	7	11407	7306	9080	4033	Bi-weekly	1695	Weekly	Weekdays 7am-9am	1 week ago	Increasing
6	0xqSKdKE0FdOeLTiHI08WfGsYLJfRz4OMDZIbFMLmV	54	10064	218	Monthly	5	16907	4248	12041	4489	Never	2352	Bi-weekly	Weekends 8pm-12am	1 month ago	Increasing
7	0xVDk4jvAWlDpaEkhMhrxZKr0p7uqpHsz7mee96Lv6	164	23546	246	Weekly	7	3104	1992	7173	2060	Monthly	930	Never	Weekdays 9am-5pm	2 days ago	Increasing
8	0xgTlUywOCKmtntLGJOQnbTn8YcJFDZPCvNIZtMqID	162	43583	260	Weekly	8	11279	7559	3514	1115	Bi-weekly	2307	Monthly	Weekdays 10am-4pm	2 weeks ago	Increasing
9	0xUhg8trjjhVhBRrIblUT0HTX4dRdn2XmlTY3aAzX4	63	5062	264	Weekly	4	18988	4557	6130	488	Weekly	1183	Monthly	Weekends 8pm-12am	1 day ago	Increasing

	Transaction Count	Transaction Volume	Avg Transaction Value	Transaction Frequency	Asset Diversity	Liquidity Provision	Staking Activity	Yield Farming Participation	Borrowing Value	Lending Value	Transaction Recency	Historical Activity Trends	Active Periods_Weekdays 10am-4pm	Active Periods_Weekdays 11am-3pm	Active Periods_Weekends 10am-2pm	Active Periods_Weekends 6pm-10pm	Active Periods_Weekends 8pm-12am
0	154	18248	274	0.0	5	5926	7232	11620	937	1065	4.0	0.0	0.0	1.0	0.0	0.0	0.0
1	23	16162	298	2.0	3	1110	6273	1872	1405	312	3.0	1.0	0.0	0.0	0.0	0.0	1.0
2	31	42378	252	2.0	5	12326	9085	3379	1913	4942	0.0	0.0	1.0	0.0	0.0	0.0	0.0
3	141	13233	300	0.0	1	19476	8981	2560	2388	200	5.0	0.0	0.0	0.0	0.0	1.0	0.0
4	112	2435	296	1.0	7	12749	5968	2967	4038	6802	0.0	2.0	0.0	0.0	1.0	0.0	0.0